Set up
suppressPackageStartupMessages({
library(tidyverse)
})
Read in data and process
tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>%
filter(!tmb >= 10) %>%
select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count, region_size, tmb, VAF)
genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>%
left_join(tmb_vaf_df, by = c("Kids_First_Biospecimen_ID"))
# Which patient samples don't have TMB?
#genomic_paired_df %>%
# filter(is.na(tmb)) %>%
# unique() %>%
# regulartable() %>%
# fontsize(size = 12, part = "all")
# Vector to order timepoints
td_order <- c("Diagnosis", "Progressive", "Recurrence", "Deceased", "Second Malignancy", "Unavailable")
df <- genomic_paired_df %>%
filter(!is.na(tmb)) %>%
mutate(tumor_descriptor = factor(tumor_descriptor),
tumor_descriptor = fct_relevel(tumor_descriptor, td_order))
# Let's count #samples per cancer groups and timepoints
timepoint_n_df <- df %>%
count(cancer_group, tumor_descriptor) %>%
dplyr::mutate(tumor_descriptor_cancer_n = glue::glue("{cancer_group}_{tumor_descriptor} (N={n})")) %>%
dplyr::rename(timepoint_n = n)
# Create df to use for plots
df_plot <- df %>%
left_join(timepoint_n_df, by = c("tumor_descriptor", "cancer_group")) #%>%
#filter(!timepoint_n <= 2)
Type of non synonymous mutations per cancer group
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE)
# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$Variant_Classification
Warning: Unknown or uninitialised column: `Variant_Classification`.
# Define label for plots
Alteration_type <- df_plot$Variant_Classification
# Define ylim
ylim <- max(df_plot$tmb)
What type of alterations we observe per tumor descriptor?
# Create bxp
print(ggpubr::ggboxplot(df_plot,
x = "tumor_descriptor",
y = "tmb",
color = "Variant_Classification",
#add = "jitter",
palette = palette) +
theme_Publication() +
scale_y_continuous(limits = c(0, ylim)) +
ylab("TMB") +
xlab("Timepoint") +
theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_timepoints.pdf",
path = plots_dir,
width = 15,
height = 8,
device = "pdf",
useDingbats = FALSE)
What type of alterations we observe per tumor descriptor in each
cancer group?
# Create bxp
print(ggpubr::ggboxplot(df_plot,
x = "tumor_descriptor",
y = "tmb",
color = "Variant_Classification",
#add = "jitter",
palette = palette) +
facet_wrap(~cancer_group) +
theme_Publication() +
ylab("TMB") +
xlab("Timepoint") +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_cancer_group.pdf",
path = plots_dir,
width = 25,
height = 18,
device = "pdf",
useDingbats = FALSE)
What type of alterations we observe per tumor descriptor in each
cancer group (add _n))?
cg <- as.character(unique(df_plot$cancer_group))
cg
[1] "CNS Embryonal tumor" "Embryonal tumor with multilayer rosettes" "Low-grade glioma"
[4] "Ependymoma" "Medulloblastoma" "Atypical Teratoid Rhabdoid Tumor"
[7] "Diffuse midline glioma" "High-grade glioma" "Ganglioglioma"
[10] NA "Meningioma" "Glial-neuronal tumor"
[13] "Pilocytic astrocytoma" "Neuroblastoma" "Ganglioneuroblastoma"
[16] "Schwannoma" "Chordoma" "Fibromyxoid lesion"
[19] "Neurofibroma/Plexiform" "Malignant peripheral nerve sheath tumor" "Choroid plexus carcinoma"
[22] "Adamantinomatous Craniopharyngioma" "Dysembryoplastic neuroepithelial tumor" "Ewing sarcoma"
[25] "Rosai-Dorfman disease" "Hemangioblastoma" "Craniopharyngioma"
[28] "Diffuse leptomeningeal glioneuronal tumor"
# Loop through variable
for (i in seq_along(cg)){
print(i)
df_sub <- df_plot %>%
filter(cancer_group == cg[i])
# Create bxp
print(ggpubr::ggboxplot(df_sub,
x = "tumor_descriptor_cancer_n",
y = "tmb",
color = "Variant_Classification",
palette = palette) +
theme_Publication() +
ylab("TMB") +
xlab("Timepoint") +
labs(title = paste(cg)) +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))
# Save the plot
#name <- paste0(cg[i], "-Alteration_type_cancer_group.pdf")
#ggsave(filename = name,
# path = plots_dir,
# width = 12,
# height = 8,
# device = "pdf",
# useDingbats = FALSE)
}
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What type of alterations we observe per tumor descriptor in each
cancer group and timepoint model?
tm <- as.character(unique(df_plot$timepoints_models))
tm
[1] "Dx-Rec" "Dx-Pro" "Dx-Dec" "Pro-Dec" "Rec-SM" "Pro-Rec" "Dx-SM" "Pro-Rec-Dec" "Dx-Pro-Rec"
[10] "Rec-Dec" "Dx-Pro-Rec-Dec" "Dx-Rec-Dec" "Dx-Pro-Dec" "Dx-Unv"
# Loop through variable
for (i in seq_along(tm)){
print(i)
df_sub <- df_plot %>%
filter(timepoints_models == tm[i])
# Create bxp
print(ggpubr::ggboxplot(df_sub,
x = "tumor_descriptor",
y = "tmb",
color = "Variant_Classification",
palette = palette) +
facet_wrap(~cancer_group) +
theme_Publication() +
ylab("TMB") +
xlab("Timepoint") +
scale_y_continuous(limits = c(0, ylim)) +
theme(axis.text.x = element_text(angle = 90)))
# Save the plot
#name <- paste0(timepoints_models[i], "-Alteration_type_cancer_group.pdf")
#ggsave(filename = name,
# path = plots_dir,
# width = 12,
# height = 8,
# device = "pdf",
# useDingbats = FALSE)
}
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sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggthemes_4.2.4 flextable_0.9.2 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[10] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] fontquiver_0.2.1 bit64_4.0.5 rprojroot_2.0.3 tools_4.2.3 backports_1.4.1 bslib_0.5.0
[7] utf8_1.2.3 R6_2.5.1 colorspace_2.1-0 withr_2.5.0 tidyselect_1.2.0 bit_4.0.5
[13] curl_5.0.2 compiler_4.2.3 textshaping_0.3.6 cli_3.6.1 xml2_1.3.5 officer_0.6.2
[19] fontBitstreamVera_0.1.1 labeling_0.4.2 sass_0.4.7 scales_1.2.1 askpass_1.2.0 systemfonts_1.0.4
[25] digest_0.6.33 rmarkdown_2.23 gfonts_0.2.0 pkgconfig_2.0.3 htmltools_0.5.5 fastmap_1.1.1
[31] rlang_1.1.1 rstudioapi_0.15.0 httpcode_0.3.0 shiny_1.7.4.1 jquerylib_0.1.4 farver_2.1.1
[37] generics_0.1.3 jsonlite_1.8.7 vroom_1.6.3 zip_2.3.0 car_3.1-2 magrittr_2.0.3
[43] Rcpp_1.0.11 munsell_0.5.0 fansi_1.0.4 abind_1.4-5 gdtools_0.3.3 lifecycle_1.0.3
[49] stringi_1.7.12 yaml_2.3.7 carData_3.0-5 plyr_1.8.8 parallel_4.2.3 promises_1.2.0.1
[55] crayon_1.5.2 hms_1.1.3 knitr_1.43 pillar_1.9.0 ggpubr_0.6.0 uuid_1.1-0
[61] ggsignif_0.6.4 reshape2_1.4.4 pkgload_1.3.2.1 crul_1.4.0 glue_1.6.2 evaluate_0.21
[67] fontLiberation_0.1.0 data.table_1.14.8 vctrs_0.6.3 tzdb_0.4.0 httpuv_1.6.11 gtable_0.3.3
[73] openssl_2.1.0 cachem_1.0.8 xfun_0.39 mime_0.12 xtable_1.8-4 broom_1.0.5
[79] rstatix_0.7.2 later_1.3.1 ragg_1.2.5 timechange_0.2.0 ellipsis_0.3.2
---
title: "Classification of Variants across paired longitudinal samples in the PBTA Cohort"
author: 'Antonia Chroni <chronia@chop.edu> and Jo Lynne Rokita <rokita@chop.edu> for D3B'
date: "2023"
output:
  html_notebook:
    toc: TRUE
    toc_float: TRUE
---

# Set up
```{r load-library}
suppressPackageStartupMessages({
  library(tidyverse)
})
```

## Directories and File Inputs/Outputs
```{r set-dir-and-file-names}
# Detect the ".git" folder -- this will be in the project root directory
# Use this as the root directory to ensure proper sourcing of functions 
# no matter where this is called from
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal")
results_dir <- file.path(analysis_dir, "results")
input_dir <- file.path(analysis_dir, "input")
files_dir <- file.path(root_dir, "analyses", "sample-distribution-analysis", "results")

# Input files
genomic_paired_file <- file.path(files_dir, "list-genomic-paired.tsv") 
tmb_vaf_file <- file.path(results_dir, "tmb_vaf_genomic.tsv")
palette_file <- file.path(root_dir, "figures", "palettes", "oncoprint_color_palette.tsv")

# File path to plot directory
plots_dir <-
  file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}

source(paste0(root_dir, "/figures/scripts/theme.R"))
```

## Read in data and process
```{r load-process-inputs}
tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>% 
  filter(!tmb >= 10) %>% 
  select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count,	region_size, tmb, VAF)

genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>% 
  left_join(tmb_vaf_df, by = c("Kids_First_Biospecimen_ID"))

# Which patient samples don't have TMB?
#genomic_paired_df %>% 
#  filter(is.na(tmb)) %>% 
#  unique() %>% 
#  regulartable() %>%
#  fontsize(size = 12, part = "all")

# Vector to order timepoints
td_order <- c("Diagnosis", "Progressive", "Recurrence", "Deceased", "Second Malignancy", "Unavailable")

df <- genomic_paired_df %>% 
  filter(!is.na(tmb)) %>% 
  mutate(tumor_descriptor = factor(tumor_descriptor),
         tumor_descriptor = fct_relevel(tumor_descriptor, td_order))


# Let's count #samples per cancer groups and timepoints 
timepoint_n_df <- df %>% 
  count(cancer_group, tumor_descriptor) %>% 
  dplyr::mutate(tumor_descriptor_cancer_n = glue::glue("{cancer_group}_{tumor_descriptor}  (N={n})")) %>% 
  dplyr::rename(timepoint_n = n) 


# Create df to use for plots
df_plot <- df %>% 
  left_join(timepoint_n_df, by = c("tumor_descriptor", "cancer_group")) #%>%
  #filter(!timepoint_n <= 2) 

``` 

# Type of non synonymous mutations per cancer group

```{r define-parameters-for-plots}
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE) 

# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$Variant_Classification

# Define label for plots
Alteration_type <- df_plot$Variant_Classification

# Define ylim
ylim <- max(df_plot$tmb)
```

# What type of alterations we observe per tumor descriptor?

```{r plot-timepoint, fig.width = 15, fig.height = 8, fig.fullwidth = TRUE}
# Create bxp
print(ggpubr::ggboxplot(df_plot, 
                        x = "tumor_descriptor", 
                        y = "tmb", 
                        color = "Variant_Classification",
                        #add = "jitter",
                        palette = palette) +
        theme_Publication() + 
        scale_y_continuous(limits = c(0, ylim)) +
        ylab("TMB") +
        xlab("Timepoint") +
        theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_timepoints.pdf", 
       path = plots_dir, 
       width = 15, 
       height = 8, 
       device = "pdf", 
       useDingbats = FALSE)
```


# What type of alterations we observe per tumor descriptor in each cancer group?

```{r plot-cancer-group, fig.width = 25, fig.height = 18, fig.fullwidth = TRUE}
# Create bxp
print(ggpubr::ggboxplot(df_plot, 
                        x = "tumor_descriptor", 
                        y = "tmb", 
                        color = "Variant_Classification",
                        #add = "jitter",
                        palette = palette) +
        facet_wrap(~cancer_group) +
        theme_Publication() + 
        ylab("TMB") +
        xlab("Timepoint") +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90)))

# Save the plot
ggsave(filename = "Alteration_type_cancer_group.pdf", 
       path = plots_dir, 
       width = 25, 
       height = 18, 
       device = "pdf", 
       useDingbats = FALSE)

```

# What type of alterations we observe per tumor descriptor in each cancer group (add _n))?
 

```{r plot-tumor_descriptor_n, fig.width = 8, fig.height = 6, fig.fullwidth = TRUE}
cg <- as.character(unique(df_plot$cancer_group))
cg

# Loop through variable
for (i in seq_along(cg)){
  print(i)
  df_sub <- df_plot %>%
      filter(cancer_group == cg[i])
  
  # Create bxp
  print(ggpubr::ggboxplot(df_sub, 
                        x = "tumor_descriptor_cancer_n", 
                        y = "tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        theme_Publication() + 
        ylab("TMB") +
        xlab("Timepoint") +
        labs(title = paste(cg)) +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)))

  # Save the plot
  #name <- paste0(cg[i], "-Alteration_type_cancer_group.pdf")
  #ggsave(filename = name, 
  #       path = plots_dir, 
  #       width = 12, 
  #       height = 8, 
  #       device = "pdf", 
  #       useDingbats = FALSE)
}
```


# What type of alterations we observe per tumor descriptor in each cancer group and timepoint model?

```{r plot-timepoint-model, fig.width = 25, fig.height = 18, fig.fullwidth = TRUE}
tm <- as.character(unique(df_plot$timepoints_models))
tm

# Loop through variable
for (i in seq_along(tm)){
  print(i)
  df_sub <- df_plot %>%
      filter(timepoints_models == tm[i])
  
  # Create bxp
  print(ggpubr::ggboxplot(df_sub, 
                        x = "tumor_descriptor", 
                        y = "tmb", 
                        color = "Variant_Classification",
                        palette = palette) +
        facet_wrap(~cancer_group) +
        theme_Publication() +
        ylab("TMB") +
        xlab("Timepoint") +
        scale_y_continuous(limits = c(0, ylim)) +
        theme(axis.text.x = element_text(angle = 90)))

   # Save the plot
   #name <- paste0(timepoints_models[i], "-Alteration_type_cancer_group.pdf")
   #ggsave(filename = name, 
   #    path = plots_dir, 
   #    width = 12, 
   #    height = 8, 
   #   device = "pdf", 
   #   useDingbats = FALSE)
}
```


```{r echo=TRUE}
sessionInfo()
```
